{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NBVKcDWHeGoR"
   },
   "source": [
    "# Unsupervised graph representation learning using Graph ConvNet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lb6FvAQ3eUNs"
   },
   "source": [
    "In this notebook we will be performing unsupervised graph representation learning using Graph ConvNet as encoder.\n",
    "\n",
    "The model embeds a graph by using stacked Graph ConvNet layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "RyweACZPHYQA"
   },
   "outputs": [],
   "source": [
    "#from networkx import karate_club_graph, to_numpy_matrix\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "from scipy.linalg import sqrtm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "G = nx.barbell_graph(m1=10, m2=4)\n",
    "\n",
    "order = np.arange(G.number_of_nodes())\n",
    "A = nx.to_numpy_matrix(G, nodelist=order)\n",
    "I = np.eye(G.number_of_nodes())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "JgSsTLzr9a4y"
   },
   "outputs": [],
   "source": [
    "np.random.seed(7)\n",
    "\n",
    "A_hat = A + np.eye(G.number_of_nodes()) # add self-connections\n",
    "\n",
    "D_hat = np.array(np.sum(A_hat, axis=0))[0]\n",
    "D_hat = np.array(np.diag(D_hat))\n",
    "D_hat = np.linalg.inv(sqrtm(D_hat))\n",
    "\n",
    "A_hat = D_hat @ A_hat @ D_hat\n",
    "\n",
    "def glorot_init(nin, nout):\n",
    "  sd = np.sqrt(6.0 / (nin + nout))\n",
    "  return np.random.uniform(-sd, sd, size=(nin, nout))\n",
    "\n",
    "class GCNLayer():\n",
    "  def __init__(self, n_inputs, n_outputs):\n",
    "      self.n_inputs = n_inputs\n",
    "      self.n_outputs = n_outputs\n",
    "      self.W = glorot_init(self.n_outputs, self.n_inputs)\n",
    "      self.activation = np.tanh\n",
    "      \n",
    "  def forward(self, A, X):\n",
    "      self._X = (A @ X).T # (N,N)*(N,n_outputs) ==> (n_outputs,N)\n",
    "      H = self.W @ self._X # (N, D)*(D, n_outputs) => (N, n_outputs)\n",
    "      H = self.activation(H)\n",
    "      return H.T # (n_outputs, N)\n",
    "\n",
    "gcn1 = GCNLayer(G.number_of_nodes(), 8)\n",
    "gcn2 = GCNLayer(8, 4)\n",
    "gcn3 = GCNLayer(4, 2)\n",
    "\n",
    "H1 = gcn1.forward(A_hat, I)\n",
    "H2 = gcn2.forward(A_hat, H1)\n",
    "H3 = gcn3.forward(A_hat, H2)\n",
    "\n",
    "embeddings = H3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 319
    },
    "id": "OhVzlenz1x97",
    "outputId": "a6659970-a1e6-4b7a-a6a6-e0903ceefe5f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def draw_graph(G, filename=None, node_size=50):\n",
    "  pos_nodes = nx.spring_layout(G)\n",
    "  nx.draw(G, pos_nodes, with_labels=False, node_size=node_size, edge_color='gray')\n",
    "  \n",
    "  pos_attrs = {}\n",
    "  for node, coords in pos_nodes.items():\n",
    "    pos_attrs[node] = (coords[0], coords[1] + 0.08)\n",
    "\n",
    "  plt.axis('off')\n",
    "  axis = plt.gca()\n",
    "  axis.set_xlim([1.2*x for x in axis.get_xlim()])\n",
    "  axis.set_ylim([1.2*y for y in axis.get_ylim()])\n",
    "\n",
    "embeddings = np.array(embeddings)\n",
    "draw_graph(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "XLgjmzRLLLcs",
    "outputId": "d056de50-0e08-49f8-ea79-fc0c4bc52778"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tDAlJUitDQpLUypCQJLUyJCRJrYYKiSQrkuxKsq95Xt4y7t4kh5Pcc1T7/0zyo+aGQw8luaBpT5LPJdmf5OEk/2SYOiVJgxn2SGIzsLuq1gG7m/V+tgBXtfRdW1UXNI+Hmrb3AOuaxybgvw9ZpyRpAMOGxGXA9mZ5O7Cx36Cq2g28MMP9fqm6/hJYlmTVMIVKkmZu2JBYWVUHm+Un6N6veqb+czOl9Jkkr23aVgOP9Yw50LT9nCSbkkwmmTx06NAALy9JanPckEhyX5JH+zwu6x1X3Ztlz/SG2dcBbwF+GVgB/NYMt6eqtlZVp6o6Z5xxxkw3lyQdw5LjDaiq9W19SZ5MsqqqDjbTQU/N5MV7jkJ+luSLwH9o1qeAs3uGrmnaJElzaNjpph3ARLM8Adw9k42nzzMkCd3zGY/27PdfN1c5/VPg+Z5AkSTNkeMeSRzHTcBtSa4Gfgx8ECBJB/hIVV3TrD9Ad1rp7yc5AFxdVTuBLyc5AwjwEPCRZr9fA94L7Ad+Cvz6kHVKkgaQ7qmExaHT6dTk5OR8lyFJYyXJnqrq9OvzE9eSpFaGhCSplSEhSWplSEiSWhkSkqRWhoQkqZUhIUlqZUhIkloZEpKkVoaEJKmVISFJamVISJJaGRKSpFaGhCSplSEhSWo1VEgkWZFkV5J9zfPylnH3Jjmc5J6j2h9I8lDzeDzJXU37xUme7+n7nWHqlCQNZtgjic3A7qpaB+xu1vvZAlx1dGNV/fOquqCqLgD+Arizp/uB6b6qunHIOiVJAxg2JC4DtjfL2+nep/rnVNVu4IW2nST5B8A7gbuGrEeSNELDhsTKqjrYLD8BrBxwPxvpHpH8pKftHUm+m+RPk7y1bcMkm5JMJpk8dOjQgC8vSepnyfEGJLkPOLNP1/W9K1VVSQa9YfaVwLae9e8Ab6qqv07yXrpHGOv6bVhVW4Gt0L3H9YCvL0nq47ghUVXr2/qSPJlkVVUdTLIKeGqmBSQ5HbgI+Jc9r/mTnuWvJbk5yelV9fRM9y9JGtyw0007gIlmeQK4e4B9XA7cU1X/b7ohyZlJ0ixf1NT5zJC1SpJmaNiQuAl4V5J9wPpmnSSdJK9MHyV5ALgduCTJgSQbevZxBXDLUfu9HHg0yXeBzwFXVJVTSZI0x7KYfvd2Op2anJyc7zIkaawk2VNVnX59fuJaktTKkJAktTIkJEmtjnsJrCQtFHc9OMWWnXt5/PARzlq2lGs3nMfGC1fPd1mLmiEhaSzc9eAU1935CEdefBmAqcNHuO7ORwAMilnkdJOksbBl595XAmLakRdfZsvOvfNU0cnBIwlJY+Hxw0dm1H6yOHfzV+n9IEOAH930vpHt3yMJSWPhrGVLZ9R+Mjg6IACqaR8VQ0LSWLh2w3ksPfWUV7UtPfUUrt1w3jxVNP/aPgo9yo9IO90kaSxMn5z26qa5ZUhIGhsbL1xtKMwxp5skaUxlhu2DMCQkaUz96Kb3/VwgjPrqJqebJGmMjTIQ+vFIQpLUypCQJLUyJCRJrQwJSVIrQ0KS1GpR3eM6ySHgx3PwUqcDT8/B64zKuNUL1jwXxq1esObZ8qaqOqNfx6IKibmSZLLtpuEL0bjVC9Y8F8atXrDm+eB0kySplSEhSWplSAxm63wXMEPjVi9Y81wYt3rBmuec5yQkSa08kpAktTIkJEmtDIkWSVYk2ZVkX/O8vGXcvUkOJ7nnqPbfTLI/SSU5fQzqPTfJN5uab03ymgVU80QzZl+SiZ72f5Xk4STfS/Kp2a53RDVfmeSRpu57Z/vfxjD1Jnl9kod6Hk8n+exs1jtszU37a5JsTfK/kvxVkg8s8HrvT7K3531+42zWO2NV5aPPA/g0sLlZ3gx8qmXcJcCvAvcc1X4hsBb4P8DpY1DvbcAVzfLngY8uhJqBFcAPm+flzfJy4DTg/wJnNOO2A5cs8JqXAE9N/3to9vUfF2q9fcbtAf7FQn6Pm77/BHyyWf6F2f7/N4J67wc6s/2+DvzzzXcBC/UB7AVWNcurgL3HGHvx0b90e/rmKiQGrpfufUqeBpY06+8Adi6EmoErgd/vWf/9pu2Xgd097VcBNy/wmk8FDgFvat7zzwObFmq9R435ReAxmotdFnLNTZ2vm+06R1jvgg4Jp5varayqg83yE8DK+SzmBAxT72nA4ap6qVk/AMzFjYRPpObVdP/TT5uubT9wXpK1SZYAG4GzZ7HWaQPXXFUvAh8FHgEeB84HvjCLtcJw73GvK4Bbq/mtNssGrjnJsmb9E0m+k+T2JLP9f3cU7/EXm6mm304yyruPDu2kvjNdkvuAM/t0Xd+7UlWVZN6vFR63emH2aq6q55J8FLgV+Fvgz4F/NEyt02ar5iSn0g2JC+lON/xX4Drgk4NXO2f/Lq6ge7Q2ErNY8xJgDfDnVfXxJB8Hfo8ha5/l9/hDVTWV5PXAn9Ct9UuDVTp6J3VIVNX6tr4kTyZZVVUHk6yiO5c8r2ax3meAZUmWNEcTa4CpIcsFRlLzFN3psWlr6B6eU1VfAb7S7GsT8PICr/mCZv//u9nXbXTnsBdqvdP7+CW6U5F7hq112izW/AzwU+DOpv124OoFXC9VNdU8v5Dkj4CLWEAh4XRTux3A9BUIE8Dd81jLiRi43mYK4RvA5YNsP4QTqXkncGmS5c1VI5c2bUxfBdK0/1tg26xXPFzNU8D5Saa/bfNdwA8WcL3TrgRumdUqX23gmpt/y1/h734hXwJ8f3bLHbzeJEumr3BrjjR/BXh0luudmfk+KbJQH3Tn6XcD+4D7gBVNewfY1jPuAbonI4/QnWfc0LR/rFl/ie7887YFXu8/BL5Fd67/duC1C+g9/jdNXfuBX+9pv4XuL4Dv01yZNQY1f4RuMDxM95fZaQu53qbvh8Bb5uL9HdF7/Cbgz5r3eDdwzkKtF3gd3avGHga+B/wX4JS5eq9P5OHXckiSWjndJElqZUhIkloZEpKkVoaEJKmVISFJamVISJJaGRKSpFb/H16VbiJlFVILAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(embeddings[:, 0], embeddings[:, 1])\n",
    "plt.savefig('embedding_gcn.png',dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C83YCCDLG-Cv"
   },
   "source": [
    "## Unsupervised GCN training using similarity graph distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lpDNVIR8fMJT"
   },
   "source": [
    "For the next example, we need to install StellarGraph, the python library we will be using to build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7BtFQ8YoL4xz",
    "outputId": "df0e9283-5201-4237-960c-9ef5391bb7b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zsh:1: no matches found: stellargraph[demos]==1.2.1\r\n"
     ]
    }
   ],
   "source": [
    "# install StellarGraph\n",
    "!pip install -q stellargraph[demos]==1.2.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "iafwVXyrL6q6"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import os\n",
    "\n",
    "import stellargraph as sg\n",
    "from stellargraph.mapper import FullBatchNodeGenerator\n",
    "from stellargraph.layer import GCN\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n",
    "from sklearn import preprocessing, model_selection\n",
    "from IPython.display import display, HTML\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VHU1UGiHfw1e"
   },
   "source": [
    "In this demo, we will be using the PROTEINS dataset, already integrated in StellarGraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "id": "zhttMYjFMu5f",
    "outputId": "15cf0fdd-8eec-41eb-b307-3e26575b692a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "Each graph represents a protein and graph labels represent whether they are are enzymes or non-enzymes. The dataset includes 1113 graphs with 39 nodes and 73 edges on average for each graph. Graph nodes have 4 attributes (including a one-hot encoding of their label), and each graph is labelled as belonging to 1 of 2 classes."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = sg.datasets.PROTEINS()\n",
    "display(HTML(dataset.description))\n",
    "graphs, graph_labels = dataset.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 315
    },
    "id": "n1A345-rMx8V",
    "outputId": "3d31a583-a43d-4478-bddc-c4da1c109228"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StellarGraph: Undirected multigraph\n",
      " Nodes: 42, Edges: 162\n",
      "\n",
      " Node types:\n",
      "  default: [42]\n",
      "    Features: float32 vector, length 4\n",
      "    Edge types: default-default->default\n",
      "\n",
      " Edge types:\n",
      "    default-default->default: [162]\n",
      "        Weights: all 1 (default)\n",
      "        Features: none\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label\n",
       "1    663\n",
       "2    450"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's print some info to better understand the dataset\n",
    "print(graphs[0].info())\n",
    "graph_labels.value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tVx9OQoSgViY"
   },
   "source": [
    "### Model definition\n",
    "It's now time to build-up the model. StellarGraph offers several utility function to load and process the dataset, as well as define the GNN model and train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "gn1egwLSgUd3"
   },
   "outputs": [],
   "source": [
    "# TODO\n",
    "generator = sg.mapper.PaddedGraphGenerator(graphs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "vBJo0MkBNCLE"
   },
   "outputs": [],
   "source": [
    "# define a GCN model containing 2 layers of size 64 and 32, respectively. \n",
    "# ReLU activation function is used to add non-linearity between layers\n",
    "gc_model = sg.layer.GCNSupervisedGraphClassification(\n",
    "    [64, 32], [\"relu\", \"relu\"], generator, pool_all_layers=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "6WYIXEO1NHdW"
   },
   "outputs": [],
   "source": [
    "inp1, out1 = gc_model.in_out_tensors()\n",
    "inp2, out2 = gc_model.in_out_tensors()\n",
    "\n",
    "vec_distance = tf.norm(out1 - out2, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "dG5WFf7LNWTL"
   },
   "outputs": [],
   "source": [
    "pair_model = Model(inp1 + inp2, vec_distance)\n",
    "embedding_model = Model(inp1, out1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "liCd_C-JKebp"
   },
   "outputs": [],
   "source": [
    "def graph_distance(graph1, graph2):\n",
    "    spec1 = nx.laplacian_spectrum(graph1.to_networkx(feature_attr=None))\n",
    "    spec2 = nx.laplacian_spectrum(graph2.to_networkx(feature_attr=None))\n",
    "    k = min(len(spec1), len(spec2))\n",
    "    return np.linalg.norm(spec1[:k] - spec2[:k])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "wN0RSDgSKtVM"
   },
   "outputs": [],
   "source": [
    "graph_idx = np.random.RandomState(0).randint(len(graphs), size=(100, 2))\n",
    "targets = [graph_distance(graphs[left], graphs[right]) for left, right in graph_idx]\n",
    "train_gen = generator.flow(graph_idx, batch_size=10, targets=targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "HQpoEAdvKzWL"
   },
   "outputs": [],
   "source": [
    "pair_model.compile(optimizers.Adam(1e-2), loss=\"mse\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "aYL3qZXYLGrX",
    "outputId": "fd0867e2-6eae-47d4-80b7-07ad5d9485e3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "history = pair_model.fit(train_gen, epochs=500, verbose=0)\n",
    "sg.utils.plot_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oArvDvO3LOXc"
   },
   "outputs": [],
   "source": [
    "embeddings = embedding_model.predict(generator.flow(graphs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jDEfCnALMFm2"
   },
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "tsne = TSNE(2)\n",
    "two_d = tsne.fit_transform(embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 267
    },
    "id": "6XUWp7ZzMMtC",
    "outputId": "2f2702c9-ab2e-424a-e6eb-d72c45eef4f5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(two_d[:, 0], two_d[:, 1], c=graph_labels.cat.codes, cmap=\"jet\", alpha=0.4)\n",
    "plt.savefig('embedding_TSNE.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "i34QgSA_P_sM"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Unsupervised_GraphML.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
